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bbd0a6b201
Migrate workflow_templates/site into the frontend monorepo as apps/hub so the hub can use @comfyorg/design-system and shared packages. Changes to existing files: - pnpm-workspace.yaml: add @astrojs/sitemap, @astrojs/vercel, lucide-vue-next - eslint.config.ts: add hub ignores and i18n/import rule overrides - .oxlintrc.json: add hub scripts to ignore patterns - knip.config.ts: add hub workspace config apps/hub adaptations from source: - Replace local cn() with @comfyorg/tailwind-utils (19 files) - Integrate @comfyorg/design-system/css/base.css in global.css - Make TEMPLATES_DIR configurable via HUB_TEMPLATES_DIR env var - Add HUB_SKIP_SYNC flag for builds without template data - Remove Vite 8-incompatible rollupOptions.output.manualChunks - Fix stylelint violations (modern color notation, number precision) - Gitignore generated content (thumbnails, synced templates, AI cache)
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Text-to-Image Generation
Text-to-image is the foundational workflow in ComfyUI: you provide a text description (prompt) and the system generates an image from scratch. This is the starting point for most generative AI art. A diffusion model iteratively denoises a random latent image, guided by your text prompt encoded through CLIP, to produce a coherent image matching your description.
How It Works in ComfyUI
- Key nodes involved:
CheckpointLoaderSimple,CLIPTextEncode(positive + negative),EmptyLatentImage,KSampler,VAEDecode,SaveImage - Typical workflow pattern: Load checkpoint → encode text prompts → create empty latent → sample → decode latent to image → save
Key Settings
- Resolution: Must match the model's training resolution. Use 512×512 for SD 1.5, 1024×1024 for SDXL and Flux models. Mismatched resolutions produce artifacts like duplicated limbs or distorted compositions.
- Steps: Number of denoising iterations. 20–30 steps is a good balance between quality and speed. More steps refine details but with diminishing returns beyond 30.
- CFG Scale: Controls how strongly the sampler follows your prompt. 7–8 is the typical range. Higher values increase prompt adherence but can introduce oversaturation or artifacts.
- Seed: Determines the initial random noise. A fixed seed produces reproducible results, which is useful for iterating on prompts while keeping composition consistent.
Tips
- Start with simple, descriptive prompts before adding stylistic modifiers. Complex prompts can conflict and produce muddy results.
- Use the negative prompt
CLIPTextEncodeto specify what you want to avoid (e.g., "blurry, low quality, deformed hands") — this significantly improves output quality. - Always match your
EmptyLatentImageresolution to the model you loaded. A 768×768 image on an SD 1.5 checkpoint will produce noticeably worse results than 512×512.